Research

Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways — they exhibit a rich temporal structure of user adoption and long-term engagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been difficult, however, to study apps in their natural setting since this requires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit.

In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties. At the temporal level, we develop a retention model that represents a user’s tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two fundamental axes — popularity and sociality — and we show how a user’s probability of adopting an app depends both on properties of the local network structure and on the match between the user’s attributes, his or her friends’ attributes, and the dominant attributes within the app’s user population. We also devolop models that show the importance of different feature sets with strong performance in predicting app success.